Additional file 1: of Genetic risk variants for brain disorders are enriched in cortical H3K27ac domains

Table S1. Details of the GWAS datasets used in this study. (PDF 46 kb

Additional file 2: of Genetic risk variants for brain disorders are enriched in cortical H3K27ac domains

Table S2. Enrichments for all seven brain traits remained significant when correcting for the number of independent tests performed. (PDF 30 kb

Additional file 1: of Early life diet conditions the molecular response to post-weaning protein restriction in the mouse

Table S1. Compositions of the control (CT) diet and the protein restricted (PR) diet. Table S2. List of all male mice studied with each litter represented by the first two numbers and letter of each ID. Table S3. Sequences of primers used for targeted analysis of DNA methylation at rDNA CpG −133 and the imprinted regions MEST, MCTS2, NESP and IGF2/H19. Figure S1. Growth rates of mice and lengths at death in each group. Figure S2. Absolute and relative organ weights. Figure S3. Absolute and relative adipose tissue deposit weights. Figure S4. Sperm small RNA size distribution analysis and sperm purity analysis. Figure S5. Read length distribution of reads mapping to the genome that also map to different classes of small RNA, normalised by total number of reads mapping to the genome. Figure S6. Maternal weight, food intake and litter size data. Figure S7. %A/C and CpG –133 meth % distribution in four diet groups. Figure S8. Growth trajectories plotted by pre-weaning litter size (including females). Figure S9. Pre-weaning litter size (including females) has no impact on CpG –133 A meth % in any of the groups. Supplementary methods. Rationale and explanation of the use of a linear model and robust standard errors to analyse the relationship between %A and CpG –133 A meth % instead of using litter averages or individuals from the same litter without correction for relatedness. R script used to perform the analysis is also included. (DOCX 1338 kb

Table1_Donor whole blood DNA methylation is not a strong predictor of acute graft versus host disease in unrelated donor allogeneic haematopoietic cell transplantation.DOCX

Allogeneic hematopoietic cell transplantation (HCT) is used to treat many blood-based disorders and malignancies, however it can also result in serious adverse events, such as the development of acute graft-versus-host disease (aGVHD). This study aimed to develop a donor-specific epigenetic classifier to reduce incidence of aGVHD by improving donor selection. Genome-wide DNA methylation was assessed in a discovery cohort of 288 HCT donors selected based on recipient aGVHD outcome; this cohort consisted of 144 cases with aGVHD grades III-IV and 144 controls with no aGVHD. We applied a machine learning algorithm to identify CpG sites predictive of aGVHD. Receiver operating characteristic (ROC) curve analysis of these sites resulted in a classifier with an encouraging area under the ROC curve (AUC) of 0.91. To test this classifier, we used an independent validation cohort (n = 288) selected using the same criteria as the discovery cohort. Attempts to validate the classifier failed with the AUC falling to 0.51. These results indicate that donor DNA methylation may not be a suitable predictor of aGVHD in an HCT setting involving unrelated donors, despite the initial promising results in the discovery cohort. Our work highlights the importance of independent validation of machine learning classifiers, particularly when developing classifiers intended for clinical use.</p

DataSheet1_Donor whole blood DNA methylation is not a strong predictor of acute graft versus host disease in unrelated donor allogeneic haematopoietic cell transplantation.DOCX

Allogeneic hematopoietic cell transplantation (HCT) is used to treat many blood-based disorders and malignancies, however it can also result in serious adverse events, such as the development of acute graft-versus-host disease (aGVHD). This study aimed to develop a donor-specific epigenetic classifier to reduce incidence of aGVHD by improving donor selection. Genome-wide DNA methylation was assessed in a discovery cohort of 288 HCT donors selected based on recipient aGVHD outcome; this cohort consisted of 144 cases with aGVHD grades III-IV and 144 controls with no aGVHD. We applied a machine learning algorithm to identify CpG sites predictive of aGVHD. Receiver operating characteristic (ROC) curve analysis of these sites resulted in a classifier with an encouraging area under the ROC curve (AUC) of 0.91. To test this classifier, we used an independent validation cohort (n = 288) selected using the same criteria as the discovery cohort. Attempts to validate the classifier failed with the AUC falling to 0.51. These results indicate that donor DNA methylation may not be a suitable predictor of aGVHD in an HCT setting involving unrelated donors, despite the initial promising results in the discovery cohort. Our work highlights the importance of independent validation of machine learning classifiers, particularly when developing classifiers intended for clinical use.</p